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The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy.

In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation.

It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process.

The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.

The purpose of this paper is to recover the waste heat of flue gas heat exchanger (FGHE) as efficiently as possible and avoid the acid dew corrosion of that.

A novel flue gas waste heat recovery system was proposed in the paper. The dynamic mathematical models of key equipment in that were established based on theory and experiment method. The proportion integration differentiation-differentiation (PID-P) cascade control method based on particle swarm optimization algorithm was used to control the outlet temperature of FGHE. The dynamic characteristics of the flue gas heat exchange system were simulated by the particle swarm optimization algorithm with different fitness functions.

The PID-P temperature controller parameters can be quickly and effectively obtained by the particle swarm optimization algorithm based on the fitness function of integral time absolute error (ITAE). The overshoot, rise time and adjusting time of the novel system are 2, 83 and 105s, respectively. Compared with the traditional two-step tuning (T-ST) method, the novel system is better in dynamic and steady-state performance. The overshoot and the adjustment time of the system are reduced by 44% and 328s, respectively. ITAE is a performance evaluation index for control system with good engineering practicability and selectivity.

The dynamic mathematical model of key equipment in the new flue gas waste heat recovery system is established and the system's control strategies and methods are explored.